This invention relates to the use of optical neural networks to control switches, in particular to the control of self-routing switches.
A switch is a device which takes N inputs and reproduces some or all of them at M outputs in some desired permutation. A switch may comprise a number of smaller switching elements which are connected together to form a larger switching function. A self-routing switch is one in which the route through the switch is not determined by a global controller but rather local routing decisions are made at each smaller switch element and therefore routing can take place very quickly.
In a packet switching network, information to be transmitted is digitised and then formed into small packets, each of which contains a destination address. The packets are transmitted over a communications network. The packets may be transmitted in asynchronous transfer mode (ATM) which is a standardised packet switching protocol.
If a switch can permute the inputs in any order then the switch is known as non-blocking, otherwise it is known as blocking. Two or more of the routes through a self-routing switch may overlap causing internal blocking. Prevention of internal blocking may be achieved either by sorting the packets so they start at different inputs, or by bypassing blocked packets, transmitting non-blocked packets which arrive at the switch after the blocked packets.
Hence, although a global controller is not needed in a self-routing switch for routing the individual packets a controller is still required to resolve potential blocking unless packets are to be lost.
It has been proposed to apply the techniques of neural networks to controlling signals in an ATM network. A general review is given in T X Brown, `Neural Networks for Switching` from E C Posner, Ed, `Special Issue on Neural Networks in Communications`, IEEE Communications Magazine, p72, November 1989; see also
A Maren, C Hartson and R Rap, `Handbook of Neural Computing Applications`, Academic Press, London, 1990.
Artificial neural networks have been employed for many applications including pattern recognition and classification, content-addressable memory and combinatorial optimisation. For a general review, see J. Hertz, A. Krogh and R. G. Palmer, "Introduction to the theory of neural computation", Addison-Wesley, Redwood City, Calif., 1991.
Electrical neural networks have been employed for control of a crossbar switch, see our patent application WO-A-94/24637, and for the control of self routing switches, see "Neural Network design of a Banyan network controller", T. X. Brown & K. H. Lee, IEEJ on Selected Areas in Communications, Vol 8, No 8, pp 1428-1438,1990.
The individual neurons of a neural network may be connected together according to a number of different known schemes. They may be arranged in layers as in the case of a multi-layer perceptron, in which all the inputs to a given neuron are derived from the layer above. Another example is the Hopfield network, in which the neurons are mutually interconnected through neural weights. Many other connection schemes are well known in the art.